The Missing Metal Problem in Galaxy Clusters: Characterizing the Early Enrichment Population
Explore the source record for details and available documents.
Anne E. Blackwell↗
SEARCH · Search NASA
Results for “cluster characterization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Near-Field Acoustical Characterization of Clustered Rocket Engines
This paper presents an approach for the prediction and characterization of the near-field acoustic levels from closely-spaced clustered rocket engines. The calculations are based on the method proposed by Eldred, wherein the flowfield from the clustered rockets is divided into two zones. Zone 1 contains the isolated nozzles which produce noise independently, and extends up to a distance where the individual flows completely mix to form an equivalent single nozzle flow. Zone 2 is occupied by the single mixed stream starting from the station where the jets merge. The acoustic fields from the two zones are computed separately on the basis of the NASA-SP method of Eldred developed for a single equivalent nozzle. A summation of the spectra for the two zones yields the total effective sound pressure level for the clustered engines. Under certain conditions of nozzle spacing and flow parameters, the combined sound pressure level spectrum for the clustered nozzles displays a double peak. Test cases are presented here to demonstrate the importance of hydrodynamic interactions responsible for the double peak in the sound spectrum in the case of clustered rocket nozzles, and the role of ground reflections in the case of non-interfering jets. A graphics interface (Rocket Acoustic Prediction Tool) has been developed to take into account the effects of clustered nozzles and ground reflections.
Kandula, Max↗
Cluster-based Characterization of Multi-dimensional Tropospheric Ozone Variability in Coastal Regions: An Analysis of Lidar Measurements and Model Results
Explore the source record for details and available documents.
Claudia Bernier↗
Mesoscale Convection Structures Associated With Airglow Patches Characterized Using Cluster‐Imager Conjunctions
Explore the source record for details and available documents.
L. V. Goodwin↗
Spatial Analysis of Great Lakes Regional Icing Cloud Liquid Water Content
Abstract Clustering of cloud microphysical conditions, such as liquid water content (LWC) and drop size, can affect the rate and shape of ice accretion and the airworthiness of aircraft. Clustering may also degrade the accuracy of cloud LWC measurements from radars and microwave radiometers being developed by the government for remotely mapping icing conditions ahead of aircraft in flight. This paper evaluates spatial clustering of LWC in icing clouds using measurements collected during NASA research flights in the Great Lakes region. We used graphical and analytical approaches to describe clustering. The analytical approach involves determining the average size of clusters and computing a clustering intensity parameter. We analyzed flight data composed of 1-s-frequency LWC measurements for 12 periods ranging from 17.4 minutes (73 km) to 45.3 minutes (190 km) in duration. Graphically some flight segments showed evidence of consistency with regard to clustering patterns. Cluster intensity varied from 0.06, indicating little clustering, to a high of 2.42. Cluster lengths ranged from 0.1 minutes (0.6 km) to 4.1 minutes (17.3 km). Additional analyses will allow us to determine if clustering climatologies can be developed to characterize cluster conditions by region, time period, or weather condition. Introduction
Ryerson, Charles C.↗
An Algorithm for Characterization of Fiber Aggregation in Composite Microstructures
Composite structures are susceptible to localized flaws, or variability, that drive global failure. To capture this variance, a multiscale model needs to be introduced that not only accurately represents the statistical nature of the composite microstructure but is also efficient. At the microscale, fiber aggregation creates local stress concentrations, where failure is likely to occur sooner than expected. A method of rapid microstructure generation, in conjunction with cluster characterization, is examined to develop accurate reproductions of 2D composite cross-sections. Parameterization of shape and size of fiber clusters is used to characterize representative volume elements. The discrete element method is used to generate pseudo-microstructures. The clustering parameters from the pseudo- and actual microstructure arrangements (obtained from micrographs) can be compared to determine the validity of the representative volume element generated with the discrete element method. Results show a promising approach to evaluating randomness of fiber distributions and how to accurately recreate microstructures for strength analysis.
Carbon fiber↗
Classification of posture maintenance data with fuzzy clustering algorithms
Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various conditions were collected in conjunction with JSC postural control studies using a Tilt-Translation Device (TTD). The University of West Florida proposed applying the Fuzzy C-Means Clustering (FCM) Algorithms to this data with a view towards identifying various states and stages. Data supplied by NASA/JSC were submitted to the FCM algorithms in an attempt to identify and characterize cluster substructure in a mixed ensemble of pre- and post-adaptational TTD data. Following several unsuccessful trials with FCM using a full 11 dimensional data set, a set of two channels (features) were found to enable FCM to separate pre- from post-adaptational TTD data. The main conclusions are that: (1) FCM seems able to separate pre- from post-TTD subject no. 2 on the one trial that was used, but only in certain subintervals of time; and (2) Channels 2 (right rear transducer force) and 8 (hip sway bar) contain better discrimination information than other supersets and combinations of the data that were tried so far.
Bezdek, James C.↗
Reducing the Volume of NASA Earth-Science Data
A computer program reduces data generated by NASA Earth-science missions into representative clusters characterized by centroids and membership information, thereby reducing the large volume of data to a level more amenable to analysis. The program effects an autonomous data-reduction/clustering process to produce a representative distribution and joint relationships of the data, without assuming a specific type of distribution and relationship and without resorting to domain-specific knowledge about the data. The program implements a combination of a data-reduction algorithm known as the entropy-constrained vector quantization (ECVQ) and an optimization algorithm known as the differential evolution (DE). The combination of algorithms generates the Pareto front of clustering solutions that presents the compromise between the quality of the reduced data and the degree of reduction. Similar prior data-reduction computer programs utilize only a clustering algorithm, the parameters of which are tuned manually by users. In the present program, autonomous optimization of the parameters by means of the DE supplants the manual tuning of the parameters. Thus, the program determines the best set of clustering solutions without human intervention.
Lee, Seungwon↗
NASA-UVA light aerospace alloy and structures technology program
The report on progress achieved in accomplishing of the NASA-UVA Light Aerospace Alloy and Structures Technology Program is presented. The objective is to conduct interdisciplinary graduate student research on the performance of next generation, light weight aerospace alloys and associated thermal gradient structures in close collaboration with researchers. The efforts will produce basic understanding of material behavior, new monolithic and composite alloys, processing methods, solid and fluid mechanics analyses, measurement advances, and a pool of educated graduate students. The presented accomplishments include: research on corrosion fatigue of Al-Li-Cu alloy 2090; research on the strengthening effect of small In additions to Al-Li-Cu alloys; research on localized corrosion of Al-Li alloys; research on stress corrosion cracking of Al-Li-Cu alloys; research on fiber-matrix reaction studies (Ti-1100 and Ti-15-3 matrices containing SCS-6, SCS-9, and SCS-10 fibers); and research on methods for quantifying non-random particle distribution in materials that has led to generation of a set of computer programs that can detect and characterize clusters in particles.
Gangloff, Richard P.↗
Automated clustering-based workload characterization
The demands placed on the mass storage systems at various federal agencies and national laboratories are continuously increasing in intensity. This forces system managers to constantly monitor the system, evaluate the demand placed on it, and tune it appropriately using either heuristics based on experience or analytic models. Performance models require an accurate workload characterization. This can be a laborious and time consuming process. It became evident from our experience that a tool is necessary to automate the workload characterization process. This paper presents the design and discusses the implementation of a tool for workload characterization of mass storage systems. The main features of the tool discussed here are: (1)Automatic support for peak-period determination. Histograms of system activity are generated and presented to the user for peak-period determination; (2) Automatic clustering analysis. The data collected from the mass storage system logs is clustered using clustering algorithms and tightness measures to limit the number of generated clusters; (3) Reporting of varied file statistics. The tool computes several statistics on file sizes such as average, standard deviation, minimum, maximum, frequency, as well as average transfer time. These statistics are given on a per cluster basis; (4) Portability. The tool can easily be used to characterize the workload in mass storage systems of different vendors. The user needs to specify through a simple log description language how the a specific log should be interpreted. The rest of this paper is organized as follows. Section two presents basic concepts in workload characterization as they apply to mass storage systems. Section three describes clustering algorithms and tightness measures. The following section presents the architecture of the tool. Section five presents some results of workload characterization using the tool.Finally, section six presents some concluding remarks.
Pentakalos, Odysseas I.↗
K-Means Cluster Study for Radiofrequency Propagation Characterization
The objective of this study is to design a simple method for mining radio frequency (RF) propagation data. The study explored the characteristics of a large dataset of propagation experiments conducted over the span of years and using several ground stations around the world. Furthermore, this study developed simple predictive models that can be used for link characterization and overall propagation behavior description, without the need for physical measurements on-site. It is understood that such statistical learning has several drawbacks in terms of accuracy and precision. K-means clustering was used to characterize the data set in a way never explored before in an attempt to create useful tools that reduce cost, time and risk. K-means clustering was used to characterize the data set. Cosine distance was used as a method to determine the optimal number for clustering each feature. Dependence and independence analysis was performed to explore intra and inter-sensitivity between the presented features, with respect to each other and time. Several predicative models were generated and evaluated with respect to a test set to assess a measure of prediction accuracy and precision. A simple method for data analysis was developed and tested as the basis for further studies and future refinement to produce optimal performing models.
Cognitive↗
An ALMA+ACA measurement of the shock in the Bullet Cluster
Context: The thermal Sunyaev-Zeldovich (SZ) effect presents a relatively new tool for characterizing galaxy cluster merger shocks, traditionally studied through X-ray observations. Widely regarded as the “textbook example” of a cluster merger bow shock, the western, most-prominent shock front in the Bullet Cluster (1E0657-56) represents the ideal test case for such an SZ study. Aims: We aim to characterize the shock properties using deep, high-resolution interferometric SZ effect observations in combination with priors from an independent X-ray analysis. Methods: Our analysis technique relies on the reconstruction of a parametric model for the SZ signal by directly and jointly fitting data from the Atacama Large Millimeter/submillimeter Array (ALMA) and Atacama Compact Array (ACA) in Fourier space. Results: The ALMA+ACA data are primarily sensitive to the electron pressure difference across the shock front. To estimate the shock Mach number M, this difference can be combined with the value for the upstream electron pressure derived from an independent Chandra X-ray analysis. In the case of instantaneous electron-ion temperature equilibration, we find M = 2.08 (+0.12,−0.12), in ≈ 2.4σ tension with the independent constraint from Chandra, M(X)= 2.74 ± 0.25. The assumption of purely adiabatic electron temperature change across the shock leads to M = 2.53 (+0.33,−0.25), in better agreement with the X-ray estimate M(X) = 2.57 ± 0.23 derived for the same heating scenario. Conclusion: We have demonstrated that interferometric observations of the thermal SZ effect provide constraints on the properties of the shock in the Bullet Cluster that are highly complementary to X-ray observations. The combination of X-ray and SZ data yields a powerful probe of the shock properties, capable of measuring M and addressing the question of electron-ion equilibration in cluster shocks. Our analysis is however limited by systematics related to the overall cluster geometry and the complexity of the post-shock gas distribution. To overcome these limitations, a simultaneous, joint-likelihood analysis of SZ and X-ray data is needed.
Luca Di Mascolo↗
Spectral reflectance of surface soils - A statistical analysis
The relationship of the physical and chemical properties of soils to their spectral reflectance as measured at six wavebands of Thematic Mapper (TM) aboard NASA's Landsat-4 satellite was examined. The results of performing regressions of over 20 soil properties on the six TM bands indicated that organic matter, water, clay, cation exchange capacity, and calcium were the properties most readily predicted from TM data. The middle infrared bands, bands 5 and 7, were the best bands for predicting soil properties, and the near infrared band, band 4, was nearly as good. Clustering 234 soil samples on the TM bands and characterizing the clusters on the basis of soil properties revealed several clear relationships between properties and reflectance. Discriminant analysis found organic matter, fine sand, base saturation, sand, extractable acidity, and water to be significant in discriminating among clusters.
Crouse, K. R.↗
Infrared laser absorption spectroscopy of the nu4 (sigma u) fundamental and associated nu11(pi u) hot band of C7 - Evidence for alternating rigidity in linear carbon clusters
The first characterization of the bending potential of the C7 cluster is reported via the observation of the v = 1(1) and v = 2 deg levels of the nu11 (pi u) bend as hot bands associated with the nu4 (sigma u) antisymmetric stretch fundamental. The lower state hot band rotational constants are measured to be 1004.4(1.3) and 1123.6(9.0) MHz, constituting a 9.3 and 22 percent increase over the ground state rotational constant, 918.89 (41) MHz. These large increases are strong quartic and sextic centrifugal distortion constants determined for the ground and nu 4 = 1 states are found to be anomalously large and negative, evidencing strong perturbations between stretching and bending modes.
Heath, J. R.↗
The Atacama Cosmology Telescope: Sunyaev-Zel'dovich-Selected Galaxy Clusters AT 148 GHz in the 2008 Survey
We report on 23 clusters detected blindly as Sunyaev-Zel'dovich (SZ) decrements in a 148 GHz, 455 deg (exp 2) map of the southern sky made with data from the Atacama Cosmology Telescope 2008 observing season. All SZ detections announced in this work have confirmed optical counterparts. Ten of the clusters are new discoveries. One newly discovered cluster, ACT-CL 10102-4915, with a redshift of 0.75 (photometric), has an SZ decrement comparable to the most massive systems at lower redshifts. Simulations of the cluster recovery method reproduce the sample purity measured by optical follow-up. In particular, for clusters detected with a signal-to-noise ratio greater than six, simulations are consistent with optical follow-up that demonstrated this subsample is 100% pure, The simulations further imply that the total sample is 80% complete for clusters with mass in excess of 6 x 10(exp 14) solar masses referenced to the cluster volume characterized by 500 times the critical density. The Compton gamma-X-ray luminosity mass comparison for the 11 best-detected clusters visually agrees with both self-similar and non-adiabatic, simulation-derived scaling laws,
Marriage, Tobias A.↗
The Morphology of Two X-Ray Luminous Clusters of Galaxy Redshift 0.8
Rosat HRI observations of two distant X-ray clusters were obtained, reduced, and analyzed. The emission from the clusters were characterized by a central surface brightness, a core radius, an ellipticity (if necessary), and a slope. The mass of the intracluster medium in the clusters was inferred for one of the clusters; for the other, the signal-to-noise and the irregularity in the cluster did not allow that inference to be made. Subsequent observations by another observer added to these observations allowed this inference.
Donahue, Megan↗
James Webb Space Telescope Studies of Dark Energy
The Hubble Space Telescope (HST) has contributed significantly to studies of dark energy. It was used to find the first evidence of deceleration at z=1.8 (Riess et al. 2001) through the serendipitous discovery of a type 1a supernova (SN1a) in the Hubble Deep Field. The discovery of deceleration at z greater than 1 was confirmation that the apparent acceleration at low redshift (Riess et al. 1998; Perlmutter et al. 1999) was due to dark energy rather than observational or astrophysical effects such as systematic errors, evolution in the SN1a population or intergalactic dust. The GOODS project and associated follow-up discovered 21 SN1a, expanding on this result (Riess et al. 2007). HST has also been used to constrain cosmological parameters and dark energy through weak lensing measurements in the COSMOS survey (Massey et al 2007; Schrabback et al 2009) and strong gravitational lensing with measured time delays (Suyu et al 2010). Constraints on dark energy are often parameterized as the equation of state, w = P/p. For the cosmological constant model, w = -1 at all times; other models predict a change with time, sometimes parameterized generally as w(a) or approximated as w(sub 0)+(1-a)w(sub a), where a = (1+z)(sup -1) is the scale factor of the universe relative to its current scale. Dark energy can be constrained through several measurements. Standard candles, such as SN1a, provide a direct measurement of the luminosity distance as a function of redshift, which can be converted to H(z), the change in the Hubble constant with redshift. An analysis of weak lensing in a galaxy field can be used to derive the angular-diameter distance from the weak-lensing equation and to measure the power spectrum of dark-matter halos, which constrains the growth of structure in the Universe. Baryonic acoustic oscillations (BAO), imprinted on the distribution of matter at recombination, provide a standard rod for measuring the cosmological geometry. Strong gravitational lensing of a time-variable source gives the angular diameter distance through measured time delays of multiple images. Finally, the growth of structure can also be constrained by measuring the mass of the largest galaxy clusters over cosmic time. HST has contributed to the study of dark energy through SN1a and gravitational lensing, as discussed above. HST has also helped to characterize galaxy clusters and the HST-measured constraints on the current Hubble constant H(sub 0) are relevant to the interpretation of dark energy measurements (Riess et al 2009a). HST has not been used to constrain BAO as the large number of galaxy redshifts required, of order 100 million, is poorly matched to HST's capabilities. As the successor to HST, the James Webb Space Telescope (JWST; Gardner et al 2006) will continue and extend HST's dark energy work in several ways.
Gardner, Jonathan P.↗
Droplet Vaporization In A Levitating Acoustic Field
Combustion experiments using arrays of droplets seek to provide a link between single droplet combustion phenomena and the behavior of complex spray combustion systems. Both single droplet and droplet array studies have been conducted in microgravity to better isolate the droplet interaction phenomena and eliminate or reduce the effects of buoyancy-induced convection. In most experiments involving droplet arrays, the droplets are supported on fibers to keep them stationary and close together before the combustion event. The presence of the fiber, however, disturbs the combustion process by introducing a source of heat transfer and asymmetry into the configuration. As the number of drops in a droplet array increases, supporting the drops on fibers becomes less practical because of the cumulative effect of the fibers on the combustion process. To eliminate the effect of the fiber, several researchers have conducted microgravity experiments using unsupported droplets. Jackson and Avedisian investigated single, unsupported drops while Nomura et al. studied droplet clouds formed by a condensation technique. The overall objective of this research is to extend the study of unsupported drops by investigating the combustion of well-characterized drop clusters in a microgravity environment. Direct experimental observations and measurements of the combustion of droplet clusters would provide unique experimental data for the verification and improvement of spray combustion models. In this work, the formation of drop clusters is precisely controlled using an acoustic levitation system so that dilute, as well as dense clusters can be created and stabilized before combustion in microgravity is begun. While the low-gravity test facility is being completed, tests have been conducted in 1-g to characterize the effect of the acoustic field on the vaporization of single and multiple droplets. This is important because in the combustion experiment, the droplets will be formed and levitated prior to ignition. Therefore, the droplets will begin to vaporize in the acoustic field thus forming the "initial conditions" for the combustion process. Understanding droplet vaporization in the acoustic field of this levitator is a necessary step that will help to interpret the experimental results obtained in low-gravity.
Ruff, G. A.↗